#SQL Select with Group by

[tutor-net-in]

https://www.tutornet.in/database-management-and-sql/sql-select-statement/

SQL SELECT Statement: The SELECT statement in SQL is used to retrieve data from one or more tables in a database. It allows you to specify which columns you want to retrieve and apply various conditions, groupings, and orderings to the data.

[nicolae]

SQL - SELECT: GROUP BY - Inspectarea grupurilor

După ce am definit grupuri cu cuvântul cheie GROUP BY, putem selecta mai multe informații despre fiecare dintre ele, de exemplu: câte persoane (rânduri) există în cadrul fiecărei familii (grup de rânduri)? SELECT lastname, count(*) -- count() este o funcție care numără valori sau rânduri FROM person GROUP BY lastname; Vedem că în micul nostru exemplu de bază de date există o familie cu trei…

[sak-shi]

Python Libraries to Learn Before Tackling Data Analysis

To tackle data analysis effectively in Python, it's crucial to become familiar with several libraries that streamline the process of data manipulation, exploration, and visualization. Here's a breakdown of the essential libraries:

 1. NumPy

   - Purpose: Numerical computing.

   - Why Learn It: NumPy provides support for large multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently.

   - Key Features:

     - Fast array processing.

     - Mathematical operations on arrays (e.g., sum, mean, standard deviation).

     - Linear algebra operations.

 2. Pandas

   - Purpose: Data manipulation and analysis.

   - Why Learn It: Pandas offers data structures like DataFrames, making it easier to handle and analyze structured data.

   - Key Features:

     - Reading/writing data from CSV, Excel, SQL databases, and more.

     - Handling missing data.

     - Powerful group-by operations.

     - Data filtering and transformation.

 3. Matplotlib

   - Purpose: Data visualization.

   - Why Learn It: Matplotlib is one of the most widely used plotting libraries in Python, allowing for a wide range of static, animated, and interactive plots.

   - Key Features:

     - Line plots, bar charts, histograms, scatter plots.

     - Customizable charts (labels, colors, legends).

     - Integration with Pandas for quick plotting.

 4. Seaborn

   - Purpose: Statistical data visualization.

   - Why Learn It: Built on top of Matplotlib, Seaborn simplifies the creation of attractive and informative statistical graphics.

   - Key Features:

     - High-level interface for drawing attractive statistical graphics.

     - Easier to use for complex visualizations like heatmaps, pair plots, etc.

     - Visualizations based on categorical data.

 5. SciPy

   - Purpose: Scientific and technical computing.

   - Why Learn It: SciPy builds on NumPy and provides additional functionality for complex mathematical operations and scientific computing.

   - Key Features:

     - Optimized algorithms for numerical integration, optimization, and more.

     - Statistics, signal processing, and linear algebra modules.

 6. Scikit-learn

   - Purpose: Machine learning and statistical modeling.

   - Why Learn It: Scikit-learn provides simple and efficient tools for data mining, analysis, and machine learning.

   - Key Features:

     - Classification, regression, and clustering algorithms.

     - Dimensionality reduction, model selection, and preprocessing utilities.

 7. Statsmodels

   - Purpose: Statistical analysis.

   - Why Learn It: Statsmodels allows users to explore data, estimate statistical models, and perform tests.

   - Key Features:

     - Linear regression, logistic regression, time series analysis.

     - Statistical tests and models for descriptive statistics.

 8. Plotly

   - Purpose: Interactive data visualization.

   - Why Learn It: Plotly allows for the creation of interactive and web-based visualizations, making it ideal for dashboards and presentations.

   - Key Features:

     - Interactive plots like scatter, line, bar, and 3D plots.

     - Easy integration with web frameworks.

     - Dashboards and web applications with Dash.

 9. TensorFlow/PyTorch (Optional)

   - Purpose: Machine learning and deep learning.

   - Why Learn It: If your data analysis involves machine learning, these libraries will help in building, training, and deploying deep learning models.

   - Key Features:

     - Tensor processing and automatic differentiation.

     - Building neural networks.

 10. Dask (Optional)

   - Purpose: Parallel computing for data analysis.

   - Why Learn It: Dask enables scalable data manipulation by parallelizing Pandas operations, making it ideal for big datasets.

   - Key Features:

     - Works with NumPy, Pandas, and Scikit-learn.

     - Handles large data and parallel computations easily.

Focusing on NumPy, Pandas, Matplotlib, and Seaborn will set a strong foundation for basic data analysis.

[sonadukane]

How to Become a Data Scientist in 2025 (Roadmap for Absolute Beginners)

Want to become a data scientist in 2025 but don’t know where to start? You’re not alone. With job roles, tech stacks, and buzzwords changing rapidly, it’s easy to feel lost.

But here’s the good news: you don’t need a PhD or years of coding experience to get started. You just need the right roadmap.

Let’s break down the beginner-friendly path to becoming a data scientist in 2025.

✈️ Step 1: Get Comfortable with Python

Python is the most beginner-friendly programming language in data science.

What to learn:

Variables, loops, functions

Libraries like NumPy, Pandas, and Matplotlib

Why: It’s the backbone of everything you’ll do in data analysis and machine learning.

🔢 Step 2: Learn Basic Math & Stats

You don’t need to be a math genius. But you do need to understand:

Descriptive statistics

Probability

Linear algebra basics

Hypothesis testing

These concepts help you interpret data and build reliable models.

📊 Step 3: Master Data Handling

You’ll spend 70% of your time cleaning and preparing data.

Skills to focus on:

Working with CSV/Excel files

Cleaning missing data

Data transformation with Pandas

Visualizing data with Seaborn/Matplotlib

This is the “real work” most data scientists do daily.

🧬 Step 4: Learn Machine Learning (ML)

Once you’re solid with data handling, dive into ML.

Start with:

Supervised learning (Linear Regression, Decision Trees, KNN)

Unsupervised learning (Clustering)

Model evaluation metrics (accuracy, recall, precision)

Toolkits: Scikit-learn, XGBoost

🚀 Step 5: Work on Real Projects

Projects are what make your resume pop.

Try solving:

Customer churn

Sales forecasting

Sentiment analysis

Fraud detection

Pro tip: Document everything on GitHub and write blogs about your process.

✏️ Step 6: Learn SQL and Databases

Data lives in databases. Knowing how to query it with SQL is a must-have skill.

Focus on:

SELECT, JOIN, GROUP BY

Creating and updating tables

Writing nested queries

🌍 Step 7: Understand the Business Side

Data science isn’t just tech. You need to translate insights into decisions.

Learn to:

Tell stories with data (data storytelling)

Build dashboards with tools like Power BI or Tableau

Align your analysis with business goals

🎥 Want a Structured Way to Learn All This?

Instead of guessing what to learn next, check out Intellipaat’s full Data Science course on YouTube. It covers Python, ML, real projects, and everything you need to build job-ready skills.

https://www.youtube.com/watch?v=rxNDw68XcE4

🔄 Final Thoughts

Becoming a data scientist in 2025 is 100% possible — even for beginners. All you need is consistency, a good learning path, and a little curiosity.

Start simple. Build as you go. And let your projects speak louder than your resume.

Drop a comment if you’re starting your journey. And don’t forget to check out the free Intellipaat course to speed up your progress!

[digitaldetoxworld]

Data Analysis: Turning Information into Insight

 In nowadays’s digital age, statistics has come to be a vital asset for businesses, researchers, governments, and people alike. However, raw facts on its personal holds little value till it's far interpreted and understood. This is wherein records evaluation comes into play. Data analysis is the systematic manner of inspecting, cleansing, remodeling, and modeling facts with the objective of coming across beneficial information, drawing conclusions, and helping selection-making.

What Is Data Analysis In Research 

What is Data Analysis?

At its middle, records analysis includes extracting meaningful insights from datasets. These datasets can variety from small and based spreadsheets to large and unstructured facts lakes. The primary aim is to make sense of data to reply questions, resolve issues, or become aware of traits and styles that are not without delay apparent.

Data evaluation is used in truely every enterprise—from healthcare and finance to marketing and education. It enables groups to make proof-based choices, improve operational efficiency, and advantage aggressive advantages.

Types of Data Analysis

There are several kinds of information evaluation, every serving a completely unique purpose:

1. Descriptive Analysis

Descriptive analysis answers the question: “What happened?” It summarizes raw facts into digestible codecs like averages, probabilities, or counts. For instance, a store might analyze last month’s sales to decide which merchandise achieved satisfactory.

2. Diagnostic Analysis

This form of evaluation explores the reasons behind beyond outcomes. It answers: “Why did it occur?” For example, if a agency sees a surprising drop in internet site visitors, diagnostic evaluation can assist pinpoint whether or not it changed into because of a technical problem, adjustments in search engine marketing rating, or competitor movements.

3. Predictive Analysis

Predictive analysis makes use of historical information to forecast destiny consequences. It solutions: “What is probable to occur?” This includes statistical models and system getting to know algorithms to pick out styles and expect destiny trends, such as customer churn or product demand.

4. Prescriptive Analysis

Prescriptive analysis provides recommendations primarily based on facts. It solutions: “What have to we do?” This is the maximum advanced type of analysis and often combines insights from predictive analysis with optimization and simulation techniques to manual selection-making.

The Data Analysis Process

The technique of information analysis commonly follows those steps:

1. Define the Objective

Before diving into statistics, it’s essential to without a doubt recognize the question or trouble at hand. A well-defined goal guides the entire analysis and ensures that efforts are aligned with the preferred outcome.

2. Collect Data

Data can come from numerous sources which includes databases, surveys, sensors, APIs, or social media. It’s important to make certain that the records is relevant, timely, and of sufficient high-quality.

3. Clean and Prepare Data

Raw information is regularly messy—it may comprise missing values, duplicates, inconsistencies, or mistakes. Data cleansing involves addressing these problems. Preparation may include formatting, normalization, or growing new variables.

Four. Analyze the Data

Tools like Excel, SQL, Python, R, or specialized software consisting of Tableau, Power BI, and SAS are typically used.

5. Interpret Results

Analysis isn't pretty much numbers; it’s about meaning. Interpreting effects involves drawing conclusions, explaining findings, and linking insights lower back to the authentic goal.

6. Communicate Findings

Insights have to be communicated effectively to stakeholders. Visualization tools including charts, graphs, dashboards, and reports play a vital position in telling the story behind the statistics.

7. Make Decisions and Take Action

The last aim of statistics analysis is to tell selections. Whether it’s optimizing a advertising marketing campaign, improving customer support, or refining a product, actionable insights flip data into real-global effects.

Tools and Technologies for Data Analysis

A big selection of gear is available for facts analysis, each suited to distinct tasks and talent levels:

Excel: Great for small datasets and short analysis. Offers capabilities, pivot tables, and charts.

Python: Powerful for complicated facts manipulation and modeling. Popular libraries consist of Pandas, NumPy, Matplotlib, and Scikit-learn.

R: A statistical programming language extensively used for statistical analysis and statistics visualization.

SQL: Essential for querying and handling information saved in relational databases.

Tableau & Power BI: User-friendly enterprise intelligence equipment that flip facts into interactive visualizations and dashboards.

Healthcare: Analyzing affected person statistics to enhance treatment plans, predict outbreaks, and control resources.

Finance: Detecting fraud, coping with threat, and guiding investment techniques.

Retail: Personalizing advertising campaigns, managing inventory, and optimizing pricing.

Sports: Enhancing performance through participant records and game analysis.

Public Policy: Informing choices on schooling, transportation, and financial improvement.

Challenges in Data Analysis

Data Quality: Incomplete, old, or incorrect information can lead to deceptive conclusions.

Data Privacy: Handling sensitive records requires strict adherence to privacy guidelines like GDPR.

Skill Gaps: There's a developing demand for skilled information analysts who can interpret complicated facts sets.

Integration: Combining facts from disparate resources may be technically hard.

Bias and Misinterpretation: Poorly designed analysis can introduce bias or lead to wrong assumptions.

The Future of Data Analysis

As facts keeps to grow exponentially, the sector of facts analysis is evolving rapidly. Emerging developments include:

Artificial Intelligence (AI) & Machine Learning: Automating evaluation and producing predictive fashions at scale.

Real-Time Analytics: Enabling decisions based totally on live data streams for faster reaction.

Data Democratization: Making records handy and understandable to everybody in an business enterprise

[lunacoding]

SQL GitHub Repositories

I’ve recently been looking up more SQL resources and found some repositories on GitHub that are helpful with learning SQL, so I thought I’d share some here!

Guides:

s-shemee SQL 101: A beginner’s guide to SQL database programming! It offers tutorials, exercises, and resources to help practice SQL

nightFuryman SQL in 30 Days: The fundamentals of SQL with information on how to set up a SQL database from scratch as well as basic SQL commands

Projects:

iweld SQL Dictionary Challenge: A SQL project inspired by a comment on this reddit thread https://www.reddit.com/r/SQL/comments/g4ct1l/what_are_some_good_resources_to_practice_sql/. This project consists of creating a single file with a column of randomly selected words from the dictionary. For this column, you can answer the various questions listed in the repository through SQL queries, or develop your own questions to answer as well.

DevMountain SQL 1 Afternoon: A SQL project where you practice inserting querying data using SQL. This project consists of creating various tables and querying data through this online tool created by DevMountain, found at this link https://postgres.devmountain.com/.

DevMountain SQL 2 Afternoon: The second part of DevMountain’s SQL project. This project involves intermediate queries such as “practice joins, nested queries, updating rows, group by, distinct, and foreign key”.

[sssit123]

How to Use SQL for Data Science Effectively

Start with the Right Training

If you're looking for the best data science training in Hyderabad, one of the first tools you should master is SQL. Structured Query Language (SQL) is fundamental to data analysis and a must-have skill for any aspiring data scientist.You can retrieve, filter, and manipulate data from relational databases with precision and speed.

Master the Basics and Go Beyond

To use SQL effectively, begin with the basics: SELECT statements, WHERE filters, JOINs, GROUP BY, and aggregate functions. These commands form the foundation for exploring and analyzing datasets. As you progress, dive into advanced topics like subqueries, common table expressions (CTEs), and window functions, which allow for more complex data transformations and analyses.

Integrate SQL with Data Science Tools

SQL pairs seamlessly with popular data science environments like Python and R. Tools such as Jupyter Notebooks allow you to run SQL queries alongside your code, creating a smooth workflow for data exploration, cleaning, and visualization. By integrating SQL with other tools, you can streamline your analysis process and enhance your productivity.

Build Scalable and Reproducible Workflows

One of the biggest advantages of using SQL in data science is its ability to support clean and reproducible workflows. SQL queries help document your data processing steps clearly, making it easier to collaborate with team members or revisit your analysis in the future.

Conclusion

Learning SQL is essential for anyone serious about a career in data science. It not only improves your ability to handle and analyze data but also strengthens your overall technical foundation. For structured learning and hands-on experience, consider enrolling at SSSiIT Computer Education, where expert-led training will prepare you for real-world data science challenges.

[govindhtech]

Build A Smarter Security Chatbot With Amazon Bedrock Agents

Use an Amazon Security Lake and Amazon Bedrock chatbot for incident investigation. This post shows how to set up a security chatbot that uses an Amazon Bedrock agent to combine pre-existing playbooks into a serverless backend and GUI to investigate or respond to security incidents. The chatbot presents uniquely created Amazon Bedrock agents to solve security vulnerabilities with natural language input. The solution uses a single graphical user interface (GUI) to directly communicate with the Amazon Bedrock agent to build and run SQL queries or advise internal incident response playbooks for security problems.

User queries are sent via React UI.

Note: This approach does not integrate authentication into React UI. Include authentication capabilities that meet your company's security standards. AWS Amplify UI and Amazon Cognito can add authentication.

Amazon API Gateway REST APIs employ Invoke Agent AWS Lambda to handle user queries.

User queries trigger Lambda function calls to Amazon Bedrock agent.

Amazon Bedrock (using Claude 3 Sonnet from Anthropic) selects between querying Security Lake using Amazon Athena or gathering playbook data after processing the inquiry.

Ask about the playbook knowledge base:

The Amazon Bedrock agent queries the playbooks knowledge base and delivers relevant results.

For Security Lake data enquiries:

The Amazon Bedrock agent takes Security Lake table schemas from the schema knowledge base to produce SQL queries.

When the Amazon Bedrock agent calls the SQL query action from the action group, the SQL query is sent.

Action groups call the Execute SQL on Athena Lambda function to conduct queries on Athena and transmit results to the Amazon Bedrock agent.

After extracting action group or knowledge base findings:

The Amazon Bedrock agent uses the collected data to create and return the final answer to the Invoke Agent Lambda function.

The Lambda function uses an API Gateway WebSocket API to return the response to the client.

API Gateway responds to React UI via WebSocket.

The chat interface displays the agent's reaction.

Requirements

Prior to executing the example solution, complete the following requirements:

Select an administrator account to manage Security Lake configuration for each member account in AWS Organisations. Configure Security Lake with necessary logs: Amazon Route53, Security Hub, CloudTrail, and VPC Flow Logs.

Connect subscriber AWS account to source Security Lake AWS account for subscriber queries.

Approve the subscriber's AWS account resource sharing request in AWS RAM.

Create a database link in AWS Lake Formation in the subscriber AWS account and grant access to the Security Lake Athena tables.

Provide access to Anthropic's Claude v3 model for Amazon Bedrock in the AWS subscriber account where you'll build the solution. Using a model before activating it in your AWS account will result in an error.

When requirements are satisfied, the sample solution design provides these resources:

Amazon S3 powers Amazon CloudFront.

Chatbot UI static website hosted on Amazon S3.

Lambda functions can be invoked using API gateways.

An Amazon Bedrock agent is invoked via a Lambda function.

A knowledge base-equipped Amazon Bedrock agent.

Amazon Bedrock agents' Athena SQL query action group.

Amazon Bedrock has example Athena table schemas for Security Lake. Sample table schemas improve SQL query generation for table fields in Security Lake, even if the Amazon Bedrock agent retrieves data from the Athena database.

A knowledge base on Amazon Bedrock to examine pre-existing incident response playbooks. The Amazon Bedrock agent might propose investigation or reaction based on playbooks allowed by your company.

Cost

Before installing the sample solution and reading this tutorial, understand the AWS service costs. The cost of Amazon Bedrock and Athena to query Security Lake depends on the amount of data.

Security Lake cost depends on AWS log and event data consumption. Security Lake charges separately for other AWS services. Amazon S3, AWS Glue, EventBridge, Lambda, SQS, and SNS include price details.

Amazon Bedrock on-demand pricing depends on input and output tokens and the large language model (LLM). A model learns to understand user input and instructions using tokens, which are a few characters. Amazon Bedrock pricing has additional details.

The SQL queries Amazon Bedrock creates are launched by Athena. Athena's cost depends on how much Security Lake data is scanned for that query. See Athena pricing for details.

Clear up

Clean up if you launched the security chatbot example solution using the Launch Stack button in the console with the CloudFormation template security_genai_chatbot_cfn:

Choose the Security GenAI Chatbot stack in CloudFormation for the account and region where the solution was installed.

Choose “Delete the stack”.

If you deployed the solution using AWS CDK, run cdk destruct –all.

Conclusion

The sample solution illustrates how task-oriented Amazon Bedrock agents and natural language input may increase security and speed up inquiry and analysis. A prototype solution using an Amazon Bedrock agent-driven user interface. This approach may be expanded to incorporate additional task-oriented agents with models, knowledge bases, and instructions. Increased use of AI-powered agents can help your AWS security team perform better across several domains.

The chatbot's backend views data normalised into the Open Cybersecurity Schema Framework (OCSF) by Security Lake.

[tpointtechedu]

SQL Tutorial | Tpoint Tech

Master SQL with this step-by-step tutorial, perfect for beginners and professionals. Learn how to write queries, manage databases, and analyze data using real-world examples. Cover key topics like SELECT, JOIN, WHERE, and GROUP BY. Build your data skills with practical exercises and clear explanations. Start your SQL journey and unlock the power of relational databases

[snaxsingh]

Master Data Analytics, SQL, and Business Intelligence

In today's fast-growing corporate environment, where most corporate decisions are made based on data, data analysis, SQL, and BI, it's not just an enhancement to your career but a survival tool. These are essential skills that anyone can cultivate, whether you are fresh out of college or even if you’re a professional seeking a change to a more specialised career outlook. And if you are searching for a career switch through a data science certification in Pune, you are already on the right path.

Now let's elucidate why Data Analytics, SQL, and Business Intelligence are significant at present, how one can learn, and the advantages you get in the real world.

Why Data Analytics, SQL, and BI Matter More Than Ever

Organisations across the globe are gathering big data at a high rate every second. However, raw data is not easily readable and hence requires analysis to extract meaning from it. That is where data analytics comes into play – to help teams understand trends, behaviors, and results. Structured Query Language (SQL) is the most common language used today to handle database information, whereas business intelligence tools provide a format for analyzing data and presenting results in an easy-to-understand format.

Here’s how they align:

Data Analytics helps uncover patterns and make predictions.

SQL allows access, organisation, and manipulation of large datasets.

BI Tools like Power BI or Tableau present data through interactive dashboards.

These are no longer niche skills. They're essential across industries, from healthcare and finance to e-commerce and logistics.

Real-Life Example: The Retail Turnaround

Take the case of a retail chain in Pune. Sales were dropping in some outlets, and the leadership had no clue why. After investing in a team equipped with data analytics skills and SQL knowledge, they began analysing customer footfall, product movement, and regional buying behaviour.

Using BI tools, the team created easy-to-understand dashboards showing that specific items weren't moving in certain regions due to pricing mismatches. Adjustments were made, promotions targeted, and within a quarter, the underperforming outlets started showing profits again. This isn’t a one-off story—it’s happening everywhere.

Build the Right Skillset

If you're considering a data science certification in Pune, make sure the curriculum covers these critical areas:

1. Data Analytics Fundamentals

Understanding basic statistics, probability, and analytical thinking is where it all begins. You'll also learn how to frame business problems and solve them using data-driven approaches.

2. SQL – Speak the Language of Databases

SQL (Structured Query Language) is the core of database management. You’ll learn how to:

Retrieve data using SELECT statements

Filter with where clauses.

Aggregate using group by and having.

Join tables efficiently

Optimise complex queries

Mastering SQL is non-negotiable if you want to dive deep into data science.

3. Business Intelligence Tools

Learning how to use tools like Tableau, Power BI, or Looker enables you to present data in visually engaging formats. Dashboards are more than pretty charts—they guide strategic decisions in real-time.

Real-Life Example: The Freelance Analyst

Ritika, a 29-year-old freelancer in Pune, was struggling to grow her consulting business. After completing a comprehensive data science course in Pune that focused on analytics, SQL, and business intelligence (BI), she offered dashboard creation and data interpretation services to startups.

Within six months, her client base doubled. She even landed a long-term contract with a US-based SaaS company to manage their product usage data. Mastering these tools didn't just make her more employable—it made her business thrive.

Career Opportunities and Salaries

Professionals with expertise in data analytics and business intelligence (BI) are in high demand. According to a recent job survey, roles that require SQL and BI skills pay 30% higher than traditional IT roles at entry-level positions. In Pune, the growing startup ecosystem and multinational presence have opened doors to exciting opportunities, especially for those who’ve undergone data science training in Pune and have hands-on skills.

Here are some career paths you can explore:

Data Analyst

Business Intelligence Developer

SQL Data Engineer

Analytics Consultant

Product Analyst

Getting Certified Makes a Difference

A data science certification in Pune not only sharpens your skills but also validates your expertise. Recruiters look for certified professionals because it reduces training time and signals that the candidate is job-ready.

Benefits of Certification:

Structured learning approach

Hands-on projects using real data

Industry-recognized credibility

Placement support (if applicable)

Peer networking and mentorship

Remember, certification is not just about learning—it's about proving that you can apply what you've learned in real-world scenarios.

Tips to Get Started

Here's how to make the most of your journey into data analytics, SQL, and BI:

Start with the basics: Brush up on statistics and Excel before diving into analytics tools.

Practice regularly: Use free datasets from Kaggle or government portals.

Join communities: Engage with local data science meetups in Pune or participate in LinkedIn groups.

Build a portfolio: Create dashboards, publish case studies, and document your learning on GitHub or Medium.

Stay updated: Tech is evolving. Stay current with the latest trends in BI tools and database technologies.

Real-Life Example: Upskilling for Career Switch

Sandeep, a mechanical engineer based in Pune, was laid off during a corporate restructuring. Rather than returning to a similar role, he decided to explore data. He mastered SQL and BI tools through a data science certification in Pune. Today, he works as a data analyst at a leading logistics firm, where he helps optimise supply chain routes using data models. What started as a setback became a turning point, all because he made the effort to upskill.

Conclusion: The Smart Career Move

Mastering data analytics, SQL, and business intelligence is no longer just for tech geeks—it's for anyone who wants to stay relevant, solve problems, and make an impact. With the rising demand for data-driven decision-making, professionals equipped with these skills are not just surviving—they're thriving.

If you're in Pune and considering stepping into this high-growth field, investing in a well-rounded data science certification in Pune can open doors you didn't even know existed. Whether it's for career transition, promotion, freelancing, or launching your startup, data is the foundation, and your journey starts now.

[datascience12pune]

Learn to Use SQL, MongoDB, and Big Data in Data Science

In today’s data-driven world, understanding the right tools is as important as understanding the data. If you plan to pursue a data science certification in Pune, knowing SQL, MongoDB, and Big Data technologies isn’t just a bonus — it’s essential. These tools form the backbone of modern data ecosystems and are widely used in real-world projects to extract insights, build models, and make data-driven decisions.

Whether you are planning on updating your resume, wanting to find a job related to analytics, or just have a general interest in how businesses apply data. Learning how to deal with structured and unstructured data sets should be a goal.

Now, analysing the relation of SQL, MongoDB, and Big Data technologies in data science and how they may transform your career, if you are pursuing data science classes in Pune.

Why These Tools Matter in Data Science?

Data that today’s data scientists use varies from transactional data in SQL databases to social network data stored in NoSQL, such as MongoDB, and data larger than the amount that can be processed by conventional means. It has to go through Big Data frameworks. That is why it is crucial for a person to master such tools:

1. SQL: The Language of Structured Data

SQL (Structured Query Language) is a widely used language to facilitate interaction between users and relational databases. Today, almost every industry globally uses SQL to solve organisational processes in healthcare, finance, retail, and many others.

How It’s Used in Real Life?

Think about what it would be like to become an employee in one of the retail stores based in Pune. In this case, you are supposed to know the trends of products that are popular in the festive season. Therefore, it is possible to use SQL and connect to the company’s sales database to select data for each product and sort it by categories, as well as to determine the sales velocity concerning the seasons. It is also fast, efficient, and functions in many ways that are simply phenomenal.

Key SQL Concepts to Learn:

SELECT, JOIN, GROUP BY, and WHERE clauses

Window functions for advanced analytics

Indexing for query optimisation

Creating stored procedures and views

Whether you're a beginner or brushing up your skills during a data science course in Pune, SQL remains a non-negotiable part of the toolkit.

2. MongoDB: Managing Flexible and Semi-Structured Data

As businesses increasingly collect varied forms of data, like user reviews, logs, and IoT sensor readings, relational databases fall short. Enter MongoDB, a powerful NoSQL database that allows you to store and manage data in JSON-like documents.

Real-Life Example:

Suppose you're analysing customer feedback for a local e-commerce startup in Pune. The feedback varies in length, structure, and language. MongoDB lets you store this inconsistent data without defining a rigid schema upfront. With tools like MongoDB’s aggregation pipeline, you can quickly extract insights and categorise sentiment.

What to Focus On?

CRUD operations in MongoDB

Aggregation pipelines for analysis

Schema design and performance optimisation

Working with nested documents and arrays

Learning MongoDB is especially valuable during your data science certification in Pune, as it prepares you for working with diverse data sources common in real-world applications.

3. Big Data: Scaling Your Skills to Handle Volume

As your datasets grow, traditional tools may no longer suffice. Big Data technologies like Hadoop and Spark allow you to efficiently process terabytes or even petabytes of data.

Real-Life Use Case:

Think about a logistics company in Pune tracking thousands of deliveries daily. Data streams in from GPS devices, traffic sensors, and delivery apps. Using Big Data tools, you can process this information in real-time to optimise routes, reduce fuel costs, and improve delivery times.

What to Learn?

Hadoop’s HDFS for distributed storage

MapReduce programming model.

Apache Spark for real-time and batch processing

Integrating Big Data with Python and machine learning pipelines

Understanding how Big Data integrates with ML workflows is a career-boosting advantage for those enrolled in data science training in Pune.

Combining SQL, MongoDB, and Big Data in Projects

In practice, data scientists often use these tools together. Here’s a simplified example:

You're building a predictive model to understand user churn for a telecom provider.

Use SQL to fetch customer plans and billing history.

Use MongoDB to analyse customer support chat logs.

Use Spark to process massive logs from call centres in real-time.

Once this data is cleaned and structured, it feeds into your machine learning model. This combination showcases the power of knowing multiple tools — a vital edge you gain during a well-rounded data science course in Pune.

How do These Tools Impact Your Career?

Recruiters look for professionals who can navigate relational and non-relational databases and handle large-scale processing tasks. Mastering these tools not only boosts your credibility but also opens up job roles like:

Data Analyst

Machine Learning Engineer

Big Data Engineer

Data Scientist

If you're taking a data science certification in Pune, expect practical exposure to SQL and NoSQL tools, plus the chance to work on capstone projects involving Big Data. Employers value candidates who’ve worked with diverse datasets and understand how to optimise data workflows from start to finish.

Tips to Maximise Your Learning

Work on Projects: Try building a mini data pipeline using public datasets. For instance, analyze COVID-19 data using SQL, store news updates in MongoDB, and run trend analysis using Spark.

Use Cloud Platforms: Tools like Google BigQuery or MongoDB Atlas are great for practising in real-world environments.

Collaborate and Network: Connect with other learners in Pune. Attend meetups, webinars, or contribute to open-source projects.

Final Thoughts

SQL, MongoDB, and Big Data are no longer optional in the data science world — they’re essential. Whether you're just starting or upgrading your skills, mastering these technologies will make you future-ready.

If you plan to enroll in a data science certification in Pune, look for programs that emphasise hands-on training with these tools. They are the bridge between theory and real-world application, and mastering them will give you the confidence to tackle any data challenge.

Whether you’re from a tech background or switching careers, comprehensive data science training in Pune can help you unlock your potential. Embrace the learning curve, and soon, you'll be building data solutions that make a real impact, right from the heart of Pune.

[shreja]

Learn SQL with Global Teq

Master SQL – The Language of Data

Are you ready to learn the language that powers every modern business?

Global Teq's career-focused SQL course takes you from zero to work-ready—in case you're starting a tech career or need to upskill for your current role.

✅ Why Learn SQL?

📊 In-demand Skill: Used in data analysis, web development, software engineering, and business intelligence.

🏢 Used by Top Companies: SQL is the foundation behind tools like Power BI, Tableau, MySQL, and Microsoft Fabric.

💼 Career Boost: Data-related roles are growing fast—and SQL is often a core requirement.

🔍 What You’ll Learn in Our Course

SQL basics: SELECT, INSERT, UPDATE, DELETE

Filtering and sorting data

Table relationships and JOIN operations

Aggregation, GROUP BY, HAVING

Writing subqueries and nested queries

Real-world scenarios with hands-on practice

📦Course Features

🕒Expert video instruction 24/7

💻Live database-based hands-on practice

📞Instructor and mentor support

🧠Ideal for those with no prior coding experience

🎓Who Should Join?

Aspiring Data Analysts and Data Engineers

IT professionals looking to reskill

Business users looking to learn about data

Students looking to join the tech industry

🚀Why Global Teq?

Global Teq is a trusted global training organization in IT and data. Our training is designed by experts and designed to make you job-ready with our skills.

🔗Learn Today

Join the thousands of learners who have elevated their careers with our SQL course.

👉Enroll Now – Try the First Module Free!

https://www.global-teq.com/

[advanto-software]

Beginner’s Guide to Data Science: What You’ll Learn at Advanto

If you're looking to break into the world of data, the Data Science Course in Pune offered by Advanto Software is your perfect starting point. Designed for complete beginners and career-switchers, this course equips you with all the essential skills needed to thrive in today’s data-driven world.

In this beginner’s guide, we'll walk you through what data science really is, why it matters, and most importantly, what you’ll learn at Advanto that makes our course stand out in Pune’s competitive tech landscape.

What is Data Science?

Data Science is the art and science of using data to drive decision-making. It involves collecting, cleaning, analyzing, and interpreting data to uncover patterns and insights that can help businesses make smarter decisions. From predicting customer behavior to optimizing supply chains, data science is everywhere.

If you're fascinated by numbers, patterns, and technology, then a Data Science Course in Pune is a fantastic career move.

Why Choose Advanto for Your Data Science Training?

At Advanto, we believe in practical learning. Our Data Science Course in Pune combines theoretical foundations with hands-on experience so that students don’t just learn — they apply. Whether you're from a coding background or completely new to tech, our structured modules and expert mentors help you build skills step by step.

Key Topics You’ll Learn in the Data Science Course at Advanto

Here’s a breakdown of the essential concepts covered in our program:

1. Python Programming

Python is the backbone of modern data science. You'll learn:

Variables, loops, and functions

Data structures like lists, tuples, and dictionaries

Libraries like NumPy, Pandas, and Matplotlib

By the end of this module, you'll be able to write scripts that automate data analysis tasks efficiently.

2. Statistics and Probability

Understanding the math behind data science is crucial. Our course simplifies:

Descriptive statistics

Inferential statistics

Probability distributions

Hypothesis testing

This foundation helps in making sense of real-world data and building predictive models.

3. Machine Learning

Here, you’ll explore how machines can learn from data using algorithms. The course covers:

Supervised and unsupervised learning

Algorithms like Linear Regression, Decision Trees, and K-Means

Model evaluation and tuning

You’ll build your first ML models and see them in action!

4. Data Visualization

Data is only as good as how you present it. Learn to use:

Matplotlib, Seaborn, and Plotly

Dashboards using Power BI or Tableau

Storytelling with data

You’ll turn complex datasets into simple, interactive visuals.

5. SQL and Databases

Learn how to work with structured data using:

SQL queries (SELECT, JOIN, GROUP BY, etc.)

Database design and normalization

Connecting SQL with Python

This is an essential skill for every data scientist.

6. Capstone Projects and Real-world Use Cases

At the end of your Data Science Course in Pune, you’ll work on real industry projects. Whether it’s predicting house prices, customer churn, or sales forecasting — your portfolio will show employers what you’re capable of.

Career Support and Certification

Our job-ready curriculum is backed by placement assistance, resume workshops, and mock interviews. Once you complete the course, you’ll receive a globally recognized certification that adds weight to your resume.

Who Can Enroll?

Our course is beginner-friendly, and anyone can join:

College students

Working professionals

Engineers and IT professionals

Career switchers

Whether you're in marketing, finance, or HR, learning data science opens new career opportunities.

Why Data Science is the Future

Data Science isn't just a trend — it’s the future. With industries increasingly relying on data, the demand for skilled professionals is skyrocketing. Pune, being a major tech hub, offers vast opportunities in this space. That’s why enrolling in a Data Science Course in Pune now can fast-track your career.

Conclusion: Take Your First Step with Advanto

The Data Science Course in Pune at Advanto Software is more than just a training program — it’s a career-launching experience. With expert faculty, hands-on projects, and strong placement support, Advanto is the right place to begin your journey into the exciting world of data science.

Visit Advanto Software to learn more and enroll today. Don’t just learn data science — live it with Advanto.

[sathcreation]

Master SQL with the Best Online Course in Hyderabad – Offered by Gritty Tech

SQL (Structured Query Language) is the backbone of data handling in modern businesses. Whether you're aiming for a career in data science, software development, or business analytics, SQL is a must-have skill. If you're based in Hyderabad or even outside but seeking the best SQL online course that delivers practical learning with real-world exposure, Gritty Tech has crafted the perfect program for you For More…

What Makes Gritty Tech's SQL Course Stand Out?

Practical, Job-Focused Curriculum

Gritty Tech’s SQL course is meticulously designed to align with industry demands. The course content is structured around the real-time requirements of IT companies, data-driven businesses, and startups.

You'll start with the basics of SQL and gradually move to advanced concepts such as:

Writing efficient queries

Managing large datasets

Building normalized databases

Using SQL with business intelligence tools

SQL for data analytics and reporting

Every module is project-based. This means you won’t just learn the theory—you’ll get your hands dirty with practical assignments that mirror real-world tasks.

Learn from Industry Experts

The faculty at Gritty Tech are not just trainers; they are seasoned professionals from top MNCs and startups. Their teaching combines theory with examples drawn from years of hands-on experience. They understand what companies expect from an SQL developer and prepare students accordingly.

Each mentor brings valuable insights into how SQL works in day-to-day business scenarios—whether it's managing millions of records in a customer database or optimizing complex queries in a financial system.

Interactive and Flexible Online Learning

Learning online doesn’t mean learning alone. Gritty Tech ensures you’re part of a vibrant student community where peer interaction, discussion forums, and collaborative projects are encouraged.

Key features of their online delivery model include:

Live instructor-led sessions with real-time query solving

Access to session recordings for future reference

Weekly challenges and hackathons to push your skills

1:1 mentorship to clarify doubts and reinforce learning

You can choose batch timings that suit your schedule, making this course ideal for both working professionals and students.

Comprehensive Module Coverage

The course is divided into logical modules that build your expertise step by step. Here's an overview of the key topics covered:

Introduction to SQL and RDBMS

Understanding data and databases

Relational models and primary concepts

Introduction to MySQL and PostgreSQL

Data Definition Language (DDL)

Creating and modifying tables

Setting primary and foreign keys

Understanding constraints and data types

Data Manipulation Language (DML)

Inserting, updating, and deleting records

Transaction management

Working with auto-commits and rollbacks

Data Query Language (DQL)

SELECT statements in depth

Filtering data with WHERE clause

Using ORDER BY, GROUP BY, and HAVING

Advanced SQL Queries

JOINS: INNER, LEFT, RIGHT, FULL OUTER

Subqueries and nested queries

Views and materialized views

Indexing and performance tuning

Stored Procedures and Triggers

Creating stored procedures for reusable logic

Using triggers to automate actions

SQL in Real Projects

Working with business databases

Creating reports and dashboards

Integrating SQL with Excel and BI tools

Interview Preparation & Certification

SQL interview Q&A sessions

Mock technical interviews

Industry-recognized certification on course completion

Real-Time Projects and Case Studies

Nothing beats learning by doing. At Gritty Tech, every student works on multiple real-time projects, such as:

Designing a complete eCommerce database

Building a report generation system for a retail chain

Analyzing customer data for a telecom company

Creating dashboards with SQL-backed queries for business decisions

These projects simulate real job roles and ensure you're not just certified but genuinely skilled.

Placement Assistance and Resume Building

Gritty Tech goes the extra mile to help you land your dream job. They offer:

Resume and LinkedIn profile optimization

Personalized career guidance

Referrals to hiring partners

Mock interview practice with real-time feedback

Graduates of Gritty Tech have successfully secured jobs as Data Analysts, SQL Developers, Business Intelligence Executives, and more at top companies.

Affordable Pricing with Installment Options

Quality education should be accessible. Gritty Tech offers this high-value SQL course at a very competitive price. Students can also opt for EMI-based payment options. There are often discounts available for early registration or referrals.

Support After Course Completion

Your learning doesn't stop when the course ends. Gritty Tech provides post-course support where you can:

Revisit lectures and materials

Get help with ongoing SQL projects at work

Stay connected with alumni and mentors

They also host webinars, advanced workshops, and alumni meetups that keep you updated and networked.

Who Should Join This Course?

This SQL course is ideal for:

College students and fresh graduates looking to boost their resume

Working professionals in non-technical roles aiming to switch to tech

Data enthusiasts and aspiring data scientists

Business analysts who want to strengthen their data querying skills

Developers who wish to enhance their backend capabilities

No prior coding experience is required. The course begins from scratch, making it beginner-friendly while progressing toward advanced topics for experienced learners.

[pallaviicert]

Introduction to Microsoft Azure Basics: A Beginner's Guide

Cloud computing has revolutionized the way businesses are run, facilitating flexibility, scalability, and innovation like never before. One of the leading cloud providers, Microsoft Azure, is a robust platform with an unparalleled set of services that cover from virtual machines and AI to database management and cybersecurity. Be it a developer, IT expert, or an interested individual curious about the cloud, getting a hold of Azure fundamentals can be your gateway into an exciting and future-proof arena. In this beginner's tutorial, we'll learn what Azure is, its fundamental concepts, and the most important services you should know to begin your cloud journey. What is Microsoft Azure? Microsoft Azure is a cloud computing service and platform that has been developed by Microsoft. It delivers many cloud-based services such as computing, analytics, storage, networking, and other services. These services are made available by selecting and using those for the purpose of building new applications as well as running already existing applications. Launched in 2010, Azure has developed at a lightning pace and now caters to hybrid cloud environments, artificial intelligence, and DevOps, becoming the go-to choice for both enterprises and startups.

Why Learn Azure? • Market Demand: Azure skills are in demand because enterprises use it heavily. • Career Growth: Azure knowledge and certifications can be a stepping stone to becoming a cloud engineer, solutions architect, or DevOps engineer. • Scalability & Flexibility: Solutions from Azure can be offered to businesses of all types, ranging from startups to Fortune 500. • Integration with Microsoft Tools: Smooth integration with offerings such as Office 365, Active Directory, and Windows Server. Fundamental Concepts of Microsoft Azure Prior to services, it would be recommended to familiarize oneself with certain critical concepts which constitute the crux of Azure.

1. Azure Regions and Availability Zones Azure can be had in every geographic area globally, regions being divided within them. Within regions, redundancy and resiliency can be had through availability zones—separate physical data centers within a region. 2. Resource Groups A resource group is a container holding Azure resources that belong together, such as virtual machines, databases, and storage accounts. It helps group and manage assets of the same lifecycle.

3. Azure Resource Manager (ARM) ARM is Azure's deployment and management service. It enables you to manage resources through templates, REST APIs, or command-line tools in a uniform way. 4. Pay-As-You-Go Model Azure has a pay-as-you-go pricing model, meaning you pay only for what you use. It also has reserved instances and spot pricing to optimize costs.

Top Azure Services That Every Beginner Should Know Azure has over 200 services. As a starter, note the most significant ones by categories like compute, storage, networking, and databases. 1. Azure Virtual Machines (VMs) Azure VMs are flexible compute resources that allow you to run Windows- or Linux-based workloads. It's essentially a computer in the cloud. Use Cases: • Hosting applications • Executing development environments • Executing legacy applications

2. Azure App Services It's a fully managed service for constructing, running, and scaling web applications and APIs. Why Use It? • Automatically scales up or down according to demand as well as remains always available • Multilanguage support (.NET, Java, Node.js, Python) • Bundled DevOps and CI/CD 3. Azure Blob Storage Blob (Binary Large Object) storage is appropriate to store unstructured data such as images, videos, and backups. Key Features: • Greatly scalable and secure • Allows data lifecycle management • Accessible using REST API

4. Azure SQL Database This is a managed relational database service on Microsoft SQL Server. Benefits: • Automatic updates and backups • Embedded high availability • Has hyperscale and serverless levels 5. Azure Functions It is a serverless computing service that runs your code in response to events. Example Use Cases: • Workflow automation • Parsing file uploads • Handling HTTP requests 6. Azure Virtual Network (VNet) A VNet is like a normal network in your on-premises environment, but it exists in Azure. Applications: • Secure communication among resources • VPN and ExpressRoute connectivity

• Subnet segmentation for better control

Getting Started with Azure 1. Create an Azure Account Start with a free Azure account with $200 credit for the initial 30 days and 12 months of free-tier services. 2. Discover the Azure Portal The Azure Portal is a web-based interface in which you can create, configure, and manage Azure resources using a graphical interface. 3. Use Azure CLI or PowerShell For command-line fans, Azure CLI and Azure PowerShell enable you to work with resources programmatically. 4. Learn with Microsoft Learn Microsoft Learn also offers interactive, role-based learning paths tailored to new users. Major Azure Management Tools Acquiring the following tools will improve your resource management ability: Azure Monitor A telemetry data gathering, analysis, and action capability for your Azure infrastructure. Azure Advisor Offers customized best practice advice to enhance performance, availability, and cost-effectiveness. Azure Cost Management + Billing Assists in tracking costs and projects costs in advance to remain within budget.

Security and Identity in Azure Azure focuses a great deal of security and compliance. 1. Azure Active Directory (Azure AD) A cloud identity and access management. You apply it to manage identities and access levels of users for Azure services. 2. Role-Based Access Control (RBAC) Allows you to define permissions for users, groups, and applications to certain resources. 3. Azure Key Vault Applied to securely store keys, secrets, and certificates.

Best Practices for Azure Beginners • Start Small: Start with straightforward services like Virtual Machines, Blob Storage, and Azure SQL Database. • Tagging: Employ metadata tags on resources for enhanced organization and cost monitoring. • Monitor Early: Use Azure Monitor and Alerts to track performance and anomalies. • Secure Early: Implement firewalls, RBAC, and encryption from the early stages. • Automate: Explore automation via Azure Logic Apps, ARM templates, and Azure DevOps.

Common Errors to Prevent • Handling cost management and overprovisioning resources lightly. • Not backing up important data. • Not implementing adequate monitoring or alerting. • Granting excessive permissions in Azure AD. • Utilizing default settings without considering security implications. Conclusion

Microsoft Azure is a strong, generic platform for supporting a large variety of usage scenarios—everything from a small web hosting infrastructure to a highly sophisticated enterprise solution. The key to successful cloud computing, however, is an understanding of the basics—ground-level concepts, primitive services, and management tools—is well-served by knowledge gained here. And that's just the start. At some point on your journey, you'll come across more complex tools and capabilities, but from a strong base, it is the secret to being able to work your way through Azure in confidence. Go ahead and claim your free account, begin trying things out, and join the cloud revolution now.

Website: https://www.icertglobal.com/course/developing-microsoft-azure-solutions-70-532-certification-training/Classroom/80/3395

[sevenmentor8]

Using a Data Analytics Institute in Pune to Unleash Data Power

Data is the backbone of every business choice in today's digital society. Maintaining competitiveness as businesses expand and diversify will need knowledge and data analysis. Anyone wishing to explore the field of data analysis should start at a Data Analytics Institute in Pune , which is a fantastic starting point. A planned curriculum, industry-relevant tools, and professional advice from these institutions help students acquire a strong knowledge of data analytics. Students can be taught how to draw insightful analysis from data with the appropriate training, hence promoting informed decision-making in many industries.

Why Select Data Analytics Training in Pune?

Known as the academic centre of India, Pune offers a really perfect place for college students wishing to improve their facts and analytics information. The Data Analytics Training in Pune objectives to empower people with the realistic expertise and equipment had to reach this area.

Training courses in Pune guarantee that students are well-prepared for the always-changing, data-driven employment market by combining theoretical knowledge with practical experience. Students not only get the technical knowledge of data analytics but also experience actual applications, hence preparing them for the industry upon graduation.

Practical Training and Industry-Relevant Curriculum

One of the main attractions of the Data Analytics Institute in Pune is its thorough programmatic fit with business needs. From machine learning to predictive analytics to data visualization, the course material is meant to give a detailed knowledge of the tools and methods employed in the data analytics scene. By including live projects and case studies in the curriculum, the colleges in Pune provide pragmatic exposure. This strategy guarantees that pupils apply theoretical ideas to address practical issues as well as understand them. Students will be competent in using well-known data analytics tools, including Python, R, SQL, and Tableau, by the end of the training.

Increasing Job Prospects in Data Analytics

Across sectors, inclusive of healthcare, banking, retail, and advertising, the want for facts analytics professionals has been continuously rising. Enrolling at a Data Analytics Institute in Pune enables human beings to qualify for an aggressive job marketplace wherein companies are actively searching out qualified facts analysts to investigate complicated information units. Graduates from reputable universities have the know-how and talents to assume positions like statistics analyst, enterprise intelligence analyst, and facts scientist. Pune, being near enormous IT centres, also provides plenty of career chances for statistics analysts.

Data Analytics' Influence on Decision-Making

Data analytics is vital for businesses if you want to make smart choices. Through the exam of sized fact sets, agencies can discover developments, patterns, and correlations that generate actionable insights. The correct Data Analytics Training in Pune enables people to apply these insights for efficient decision-making. Data-pushed methods, for example, can help agencies forecast destiny marketplace traits, decorate customer enjoyment, or maximize their advertising and marketing plans. The demand for equipped statistics employees has in no way been more crucial as groups depend increasingly more on records to fuel growth.

Busy professionals' flexible learning choices

For working professionals who want to upskill or change careers, Data Analytics Training in Pune's flexibility makes it a desirable choice. Many colleges provide online training possibilities, evening sessions, and weekend batches that fit workers with busy schedules. These flexible learning paths guarantee that people may follow their love for data analytics without sacrificing their present employment. Moreover, the accessibility of virtual labs and online materials lets students hone their abilities at their speed, hence facilitating more accessible and practical education.

Conclusion

Signing up for Data Analytics Training in Pune opens the door to a bright future in a fast-expanding sector. Pune has emerged as a centre for data analytics education in India with its industry-relevant curriculum, knowledgeable teachers, and hands-on training. Joining an institution like sevenmentor.com helps students not only acquire education but also get professional advice to enable them to find the ideal employment in this demanding sector. Pune has the tools and chances to help you thrive in the data analytics field, whether you want to improve your current abilities or launch a new career.